verbashield-mcp
Server Quality Checklist
Latest release: v1.0.1
- Disambiguation5/5
Each tool performs a distinct function: humanize transforms text, detect analyzes text, and get_quota retrieves account usage. There is no overlap or ambiguity between the three tools.
Naming Consistency5/5All tools share the verbashield_ prefix and use lowercase snake_case. The pattern is consistent and predictable: verbashield_humanize, verbashield_detect, and verbashield_get_quota.
Tool Count5/5Three tools is well-scoped for a narrow service wrapper around VerbaShield, covering the primary actions and account quota without unnecessary bulk.
Completeness5/5The tool set covers the full lifecycle for this service: text transformation, detection, and quota monitoring. No obvious gaps exist for the stated domain.
Average 3.2/5 across 3 of 3 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
This server has been verified by its author.
Add related servers to improve discoverability.
How to sync the server with GitHub?
Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It does not explain whether text is rewritten and returned, how formatting preservation works, whether any rate limits or auth constraints apply, or what side effects exist. Only the basic operation is stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and has no wasted words, but it is under-specified for a tool with three parameters and no output schema. It is concise rather than wisely structured, lacking essential semantic layers.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations, zero schema description coverage, and no output schema, the description is severely incomplete. It does not explain mode behavior, formatting preservation, return value shape, or operational constraints, so an agent cannot confidently invoke it with correct parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description provides no explanation of the three parameters. It does not clarify what 'mode' values mean, what 'text' expects, or what 'preserve_formatting' controls, leaving agents with only raw type/enum information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('humanize') applied to a clear resource ('AI-generated text'). This distinguishes it from siblings like verbashield_detect and verbashield_get_quota, though not explicitly; the verb itself makes the core purpose clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to choose this tool over alternatives, and there are no exclusions or prerequisite conditions. The name and description only imply a use case; nothing tells the agent how this relates to verbashield_detect or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided and description only states the core purpose. Does not disclose output format, input length limits, model constraints, or any other behavioral traits. Minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, no waste, concise while stating purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a detection API, crucial context is missing: what the response looks like (probability, label, confidence), whether there are length limits. No annotations and no output schema, so the description carries the burden. Sibling names help, but not enough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Single 'text' parameter has no schema description (0% coverage), and description adds no guidance on expected length, language, or content. Only tells the agent that text is required.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
States specific verb 'Detect' and resource 'AI-generated text via VerbaShield'. Clearly distinguishes from sibling verbashield_humanize which transforms text. No detailed scope but adequate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Implied usage via 'Detect' and the sibling names, but no explicit when-to-use guidance or when-not-to-use. Agent can infer it's for detection versus humanization, but no explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. The verb 'Return' clearly signals a read-only operation, and the description states the three pieces of data returned. It does not disclose output formats or units, but for a parameterless read tool this is reasonably transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the action and the returned data. There is no redundant wording or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless tool, the description is largely sufficient: an agent can invoke it immediately and knows what to expect in the response. Minor gaps such as value formats or units are not critical for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. There is no parameter information needed in the description; the empty schema already conveys that no arguments are required.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Return') and names the exact data exposed: remaining VerbaShield quota, bonus balance, and rate limit. It is clearly distinguishable from sibling tools 'verbashield_humanize' and 'verbashield_detect', which imply text transformation and detection rather than quota lookup.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The intended use is implied: call this tool when the agent needs quota, bonus, or rate-limit information. However, the description provides no explicit guidance about when to use it vs alternatives, nor any prerequisites or conditions that indicate the right time to call it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
Card Badge
Copy to your README.md:
Score Badge
Copy to your README.md: